Four for Friday: The Marriage of Recruiting and Data Science

Mike Roberts April 17, 2015

Every Friday, The DDR shines a light on the best data-centric articles and resources from across the internet, social media, and industry influencers.

Talent acquisition has experienced dramatic changes in recent years, probably more-so than any other aspect of human resources. It’s not necessarily that candidates are changing. Rather, it’s the way they are connecting with companies and finding requisitions. At the same time, the technology and tools recruiters have are on a whole new level—which leads us to our discussion here today.

With recruiting analytics, we are on the brink of an incredible transformation in the way talent acquisition professionals make decisions. Janine Truitt discussed this topic on the Data-Driven Recruiter this week, and we’ll continue on that path below in today’s roundup.

Some interesting data was recently released by KPMG. It showed that 55% of C-level and HR leaders are (still) skeptical about the potential for big data and analytics. However, 8 in ten of those same respondents expect their organization to “either begin or increase the use of Big Data and advanced analytics over the next three years.” Clearly the market still needs some education, but the maturity of the technology will certainly help guide more HR leaders toward the use of data and analytics.

Last week, the Association for Talent Development discussed a somewhat alternative approach to using data to make better decisions. Laurie Bassi and Dan McMurrer explained why it’s often better to start with your ideal end-goal in mind. They said, “Otherwise, your HR analytics efforts run the risk of devolving into another HR-check-the-box reporting initiative long on activity and short on value.” The article goes into detail about an HR analytics strategy.

Rawn Shah shared a few examples about how companies are going beyond just traditional uses of data to improve recruitment and hiring efforts. He said, given just a job description, you could use data to “figure out how hard it is to fill this role, what is the supply, who is also hiring that role, which cities, what salary ranges, and what job roles.” This is a good example of how historical data can be used in conjunction with other data sources to provide more context and even help recruiters be more proactive in their search.

While most decisions in the realm of talent acquisition in the past were based solely on gut feelings and previous experience, this article dives into the connections between recruiting talent and behavioral science. It looks forward into how recruiters can use tools like assessments and other data sources to more effectively place talent in the right positions.